class_docstr: Beam search decoder for RNN-T model with biasing support.
  
  Args:
      model (RNNT): RNN-T model to use.
      blank (int): index of blank token in vocabulary.
      temperature (float, optional): temperature to apply to joint network output.
          Larger values yield more uniform samples. (Default: 1.0)
      hypo_sort_key (Callable[[Hypothesis], float] or None, optional): callable that computes a score
          for a given hypothesis to rank hypotheses by. If ``None``, defaults to callable that returns
          hypothesis score normalized by token sequence length. (Default: None)
      step_max_tokens (int, optional): maximum number of tokens to emit per input time step. (Default: 100)
      trie (list, optional): the prefix tree for TCPGen biasing
      biasing (bool, optional): If true, do biasing, otherwise use standard RNN-T support
code: def _init_b_hypos(
      self, hypo: Optional[Hypothesis], device: torch.device
  ) -> List[Hypothesis]:
      if hypo is not None:
          token = _get_hypo_tokens(hypo)[-1]
          state = _get_hypo_state(hypo)
      else:
          token = self.blank
          state = None
  
      one_tensor = torch.tensor([1], device=device)
      pred_out, _, pred_state = self.model.predict(
          torch.tensor([[token]], device=device), one_tensor, state
      )
      init_hypo = (
          [token],
          pred_out[0].detach(),
          pred_state,
          0.0,
          self.resettrie,
      )
      return [init_hypo]
  
imports: from typing import Callable, Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import RNNT
  from torchaudio.prototype.models.rnnt import TrieNode
label_desc: Use image node_id 2 for calling the RNNTBeamSearchBiasing obj's underlying member method code with example usage: obj._init_b_hypos(hypo, device) and returns: list